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Design and Analysis of a Microbiome Mock Community: Understanding and Mitigating Methodological Biases
Author(s) -
Mosby Suquoia,
Kiflezghi Michael,
Edwards David,
Brooks Paul J,
Rivera Maria
Publication year - 2017
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.31.1_supplement.940.10
Subject(s) - microbiome , computational biology , metagenomics , biology , identification (biology) , human microbiome , human microbiome project , evolutionary biology , data science , bioinformatics , computer science , genetics , ecology , gene
The gut microbiome has been shown to influence immunity, to play a role in obesity, colorectal cancer and diabetes among others. Although much research has been done, there is still a lack of understanding of what factors in the gut microbiome are influencing the health and/or disease state of the host. Among the challenges facing microbiome research are the difficulties in accurately identifying and quantitating the components of a microbiome. A typical protocol for analyzing and comparing microbiomes is to conduct a taxonomic and phylogenetic survey or census of the members of the microbiome community. The microbiome survey is typically performed by high‐throughput sequencing of PCR amplification of the 16S rRNA gene from the community metagenome, followed by applying bioinformatics methodology to assign a taxonomic identification to each one of the sequenced fragments. These processing steps can introduce bias that can distort the true or original composition of the microbiome. The ultimate goal of the experiments presented here is to understand the type and magnitude of the bias introduced by the typical protocols used for microbiome characterization. In order to uncover potential biases affecting microbiome studies we are designing and analyzing a mock community or a synthetic microbiome. The mock community is designed by combining in vitro known proportions of a set of well‐characterized bacterial species. Processing the mock community with the same protocols used by microbiome experiments will allow us to generate a quality control for these types of experiments. We designed a low complexity mock community composed of 10 species of bacteria isolated from different hosts. These 10 bacterial species will be mixed at different ratios and the different mixes will be subjected to a full microbiome analysis. This analysis includes DNA extraction, PCR amplification, NGS sequencing and bioinformatics analysis. The goal of the experimental design is to determine our ability to recover the original input ratios. The ratios obtained by our experimental protocols will ultimately be used to develop mathematical models to predict the “true” composition of the microbiomes. In the initial phase of the project we grew each organism individually as a pure culture followed by PCR amplification and sequencing to assess the purity of the cultures. We analyzed the sequences by comparing them to the NCBI nucleotide database using BLASTN. The BLASTN results indicated we had to distinct cultures sharing only 95% sequenced identity; the sequence of one of the cultures showed a 99% sequence identity to Escherichia coli and the other showed 99% sequence identity to Shimwellia blattae. The same methodology will be used to analyze another 8 pure cultures. Support or Funding Information Initiative for Maximizing Student Development R25GM090084